0000000000712490
AUTHOR
Rebekka Olsson Omslandseter
showing 8 related works from this author
User Grouping and Power Allocation in NOMA Systems: A Reinforcement Learning-Based Solution
2020
In this paper, we present a pioneering solution to the problem of user grouping and power allocation in Non-Orthogonal Multiple Access (NOMA) systems. There are two fundamentally salient and difficult issues associated with NOMA systems. The first involves the task of grouping users together into the pre-specified time slots. The subsequent second phase augments this with the solution of determining how much power should be allocated to the respective users. We resolve this with the first reported Reinforcement Learning (RL)-based solution, which attempts to solve the partitioning phase of this issue. In particular, we invoke the Object Migration Automata (OMA) and one of its variants to re…
The Hierarchical Discrete Learning Automaton Suitable for Environments with Many Actions and High Accuracy Requirements
2022
Author's accepted manuscript Since its early beginning, the paradigm of Learning Automata (LA), has attracted much interest. Over the last decades, new concepts and various improvements have been introduced to increase the LA’s speed and accuracy, including employing probability updating functions, discretizing the probability space, and implementing the “Pursuit” concept. The concept of incorporating “structure” into the ordering of the LA’s actions is one of the latest advancements to the field, leading to the ϵ-optimal Hierarchical Continuous Pursuit LA (HCPA) that has superior performance to other LA variants when the number of actions is large. Although the previously proposed HCPA is …
User grouping and power allocation in NOMA systems: a novel semi-supervised reinforcement learning-based solution
2022
Author's accepted manuscript In this paper, we present a pioneering solution to the problem of user grouping and power allocation in non-orthogonal multiple access (NOMA) systems. The problem is highly pertinent because NOMA is a well-recognized technique for future mobile radio systems. The salient and difcult issues associated with NOMA systems involve the task of grouping users together into the prespecifed time slots, which are augmented with the question of determining how much power should be allocated to the respective users. This problem is, in and of itself, NP-hard. Our solution is the frst reported reinforcement learning (RL)-based solution, which attempts to resolve parts of thi…
The Hierarchical Discrete Pursuit Learning Automaton: A Novel Scheme With Fast Convergence and Epsilon-Optimality
2022
Author's accepted manuscript © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Since the early 1960s, the paradigm of learning automata (LA) has experienced abundant interest. Arguably, it has also served as the foundation for the phenomenon and field of reinforcement learning (RL). Over the decades, new concepts and fundamental principles have been introduced t…
Learning Automata-Based Object Partitioning with Pre-Specified Cardinalities
2020
Master's thesis in Information- and communication technology (IKT591) The Object Migrating Automata (OMA) has been used as a powerful AI-based tool to resolve real-life partitioning problems. Apart from its original version, variants and enhancements that invoke the pursuit concept of Learning Automata, and the phenomena of transitivity, have more recently been used to improve its power. The single major handicap that it possesses is the fact that the number of the objects in each partition must be equal. This thesis deals with the task of relaxing this constraint. Thus, in this thesis, we will consider the problem of designing OMA-based schemes when the number of the objects can be unequal…
Field Measurements and Parameter Calibrations of Propagation Model for Digital Audio Broadcasting in Norway
2018
During 2017, digital audio broadcasting (DAB) replaces frequency modulation (FM) broadcasting and becomes the only technology for national terrestrial audio broadcasting services in Norway. As Norway is the first country that replaces FM completely with DAB, it is of great importance to measure the signal strength of such a technology in massive deployments and to tune a simulation model as a reference for future studies. Therefore, field measurements of received signal strength are carried out in a typical Norwegian area in this work. Based on the data obtained from the measurements, a simulator with a recent empirical propagation model, namely, ITU-R P.1546-5, has been calibrated. The fin…
Object Migration Automata for Non-equal Partitioning Problems with Known Partition Sizes
2021
Part 4: Automated Machine Learning; International audience; Solving partitioning problems in random environments is a classic and challenging task, and has numerous applications. The existing Object Migration Automaton (OMA) and its proposed enhancements, which include the Pursuit and Transitivity phenomena, can solve problems with equi-sized partitions. Currently, these solutions also include one where the partition sizes possess a Greatest Common Divisor (GCD). In this paper, we propose an OMA-based solution that can solve problems with both equally and non-equally-sized groups, without restrictions on their sizes. More specifically, our proposed approach, referred to as the Partition Siz…
A Learning-Automata Based Solution for Non-equal Partitioning: Partitions with Common GCD Sizes
2021
The Object Migration Automata (OMA) has been used as a powerful tool to resolve real-life partitioning problems in random Environments. The virgin OMA has also been enhanced by incorporating the latest strategies in Learning Automata (LA), namely the Pursuit and Transitivity phenomena. However, the single major handicap that it possesses is the fact that the number of objects in each partition must be equal. Obviously, one does not always encounter problems with equally-sized groups (When the true underlying problem has non-equally-sized groups, the OMA reports the best equally-sized solution as the recommended partition.). This paper is the pioneering attempt to relax this constraint. It p…